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Merge branch 'main' into brandon/improve-llm-structured-output

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Lorenze Jay
2025-02-04 13:44:28 -08:00
committed by GitHub
parent 3de4653023 515478473a
commit 75bd0310f3
15 changed files with 236 additions and 26 deletions
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4
docs/concepts/crews.mdx
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@@ -279,9 +279,9 @@ print(result)
Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
- `kickoff()`: Starts the execution process according to the defined process flow.
- `kickoff_for_each()`: Executes tasks for each agent individually.
- `kickoff_for_each()`: Executes tasks sequentially for each provided input event or item in the collection.
- `kickoff_async()`: Initiates the workflow asynchronously.
- `kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
- `kickoff_for_each_async()`: Executes tasks concurrently for each provided input event or item, leveraging asynchronous processing.
```python Code
# Start the crew's task execution

12
docs/concepts/memory.mdx
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@@ -185,7 +185,7 @@ my_crew = Crew(
process=Process.sequential,
memory=True,
verbose=True,
embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"),
embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model="text-embedding-3-small"),
)
```
@@ -224,7 +224,7 @@ my_crew = Crew(
"provider": "google",
"config": {
"api_key": "<YOUR_API_KEY>",
"model_name": "<model_name>"
"model": "<model_name>"
}
}
)
@@ -247,7 +247,7 @@ my_crew = Crew(
api_base="YOUR_API_BASE_PATH",
api_type="azure",
api_version="YOUR_API_VERSION",
model_name="text-embedding-3-small"
model="text-embedding-3-small"
)
)
```
@@ -268,7 +268,7 @@ my_crew = Crew(
project_id="YOUR_PROJECT_ID",
region="YOUR_REGION",
api_key="YOUR_API_KEY",
model_name="textembedding-gecko"
model="textembedding-gecko"
)
)
```
@@ -288,7 +288,7 @@ my_crew = Crew(
"provider": "cohere",
"config": {
"api_key": "YOUR_API_KEY",
"model_name": "<model_name>"
"model": "<model_name>"
}
}
)
@@ -308,7 +308,7 @@ my_crew = Crew(
"provider": "voyageai",
"config": {
"api_key": "YOUR_API_KEY",
"model_name": "<model_name>"
"model": "<model_name>"
}
}
)

4
docs/concepts/planning.mdx
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@@ -81,8 +81,8 @@ my_crew.kickoff()
3. **Collect Data:**
- Search for the latest papers, articles, and reports published in 2023 and early 2024.
- Use keywords like "Large Language Models 2024", "AI LLM advancements", "AI ethics 2024", etc.
- Search for the latest papers, articles, and reports published in 2024 and early 2025.
- Use keywords like "Large Language Models 2025", "AI LLM advancements", "AI ethics 2025", etc.
4. **Analyze Findings:**

4
docs/concepts/tasks.mdx
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@@ -69,7 +69,7 @@ research_task:
description: >
Conduct a thorough research about {topic}
Make sure you find any interesting and relevant information given
the current year is 2024.
the current year is 2025.
expected_output: >
A list with 10 bullet points of the most relevant information about {topic}
agent: researcher
@@ -155,7 +155,7 @@ research_task = Task(
description="""
Conduct a thorough research about AI Agents.
Make sure you find any interesting and relevant information given
the current year is 2024.
the current year is 2025.
""",
expected_output="""
A list with 10 bullet points of the most relevant information about AI Agents